Quantitative Biomarkers for Prediction of Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer

OBJECTIVES: To predict epidermal growth factor receptor (EGFR) mutation status using quantitative radiomic biomarkers and representative clinical variables. METHODS: The study included 180 patients diagnosed as of non-small cell lung cancer (NSCLC) with their pre-therapy computed tomography (CT) scans. Using a radiomic method, 485 features that reflect the heterogeneity and phenotype of tumors were extracted. Afterwards, these radiomic features were used for predicting epidermal growth factor receptor (EGFR) mutation status by a least absolute shrinkage and selection operator (LASSO) based on multivariable logistic regression. As a result, we found that radiomic features have prognostic ability in EGFR mutation status prediction. In addition, we used radiomic nomogram and calibration curve to test the performance of the model. RESULTS: Multivariate analysis revealed that the radiomic features had the potential to build a prediction model for EGFR mutation. The area under the receiver operating characteristic curve (AUC) for the training cohort was 0.8618, and the AUC for the validation cohort was 0.8725, which were superior to prediction model that used clinical variables alone. CONCLUSION: Radiomic features are better predictors of EGFR mutation status than conventional semantic CT image features or clinical variables to help doctors to decide who need EGFR tyrosine kinase inhibitor (TKI) treatment.     

Building CT Radiomics Based Nomogram for Preoperative Esophageal Cancer Patients Lymph Node Metastasis Prediction

PURPOSE: To build and validate a radiomics-based nomogram for the prediction of pre-operation lymph node (LN) metastasis in esophageal cancer. PATIENTS AND METHODS: A total of 197 esophageal cancer patients were enrolled in this study, and their LN metastases have been pathologically confirmed. The data were collected from January 2016 to May 2016; patients in the first three months were set in the training cohort, and patients in April 2016 were set in the validation cohort. About 788 radiomics features were extracted from computed tomography (CT) images of the patients. The elastic-net approach was exploited for dimension reduction and selection of the feature space. The multivariable logistic regression analysis was adopted to build the radiomics signature and another predictive nomogram model. The predictive nomogram model was composed of three factors with the radiomics signature, where CT reported the LN number and position risk level. The performance and usefulness of the built model were assessed by the calibration and decision curve analysis. RESULTS: Thirteen radiomics features were selected to build the radiomics signature. The radiomics signature was significantly associated with the LN metastasis (P<0.001). The area under the curve (AUC) of the radiomics signature performance in the training cohort was 0.806 (95% CI: 0.732-0.881), and in the validation cohort it was 0.771 (95% CI: 0.632-0.910). The model showed good discrimination, with a Harrell’s Concordance Index of 0.768 (0.672 to 0.864, 95% CI) in the training cohort and 0.754 (0.603 to 0.895, 95% CI) in the validation cohort. Decision curve analysis showed our model will receive benefit when the threshold probability was larger than 0.15. CONCLUSION: The present study proposed a radiomics-based nomogram involving the radiomics signature, so the CT reported the status of the suspected LN and the dummy variable of the tumor position. It can be potentially applied in the individual preoperative prediction of the LN metastasis status in esophageal cancer patients.     

Deep radiomic model based on the sphere–shell partition for predicting treatment response to chemotherapy in lung cancer

BackgroundThe prognosis of chemotherapy is important in clinical decision-making for non-small cell lung cancer (NSCLC) patients.ObjectivesTo develop a model for predicting treatment response to chemotherapy in NSCLC patients from pre-chemotherapy CT images.Materials and MethodsThis retrospective multicenter study enrolled 485 patients with NSCLC who received chemotherapy alone as a first-line treatment. Two integrated models were developed using radiomic and deep-learning-based features. First, we partitioned pre-chemotherapy CT images into spheres and shells with different radii around the tumor (0–3, 3–6, 6–9, 9–12, 12–15 mm) containing intratumoral and peritumoral regions. Second, we extracted radiomic and deep-learning-based features from each partition. Third, using radiomic features, five sphere–shell models, one feature fusion model, and one image fusion model were developed. Finally, the model with the best performance was validated in two cohorts.ResultsAmong the five partitions, the model of 9–12 mm achieved the highest area under the curve (AUC) of 0.87 (95% confidence interval: 0.77–0.94). The AUC was 0.94 (0.85–0.98) for the feature fusion model and 0.91 (0.82–0.97) for the image fusion model. For the model integrating radiomic and deep-learning-based features, the AUC was 0.96 (0.88–0.99) for the feature fusion method and 0.94 (0.85–0.98) for the image fusion method. The best-performing model had an AUC of 0.91 (0.81–0.97) and 0.89 (0.79–0.93) in two validation sets, respectively.ConclusionsThis integrated model can predict the response to chemotherapy in NSCLC patients and assist physicians in clinical decision-making.     

Comparison of radiomic features in diagnostic CT images with and without contrast enhancement in the delayed phase for NSCLC patients

PurposeTo compare radiomic features extracted from diagnostic computed tomography (CT) images with and without contrast enhancement in delayed phase for non-small cell lung cancer (NSCLC) patients.MethodsDiagnostic CT images from 269 tumors [non-contrast CT, 188 (dataset NE); contrast-enhanced CT, 81 (dataset CE)] were enrolled in this study. Eighteen first-order and seventy-five texture features were extracted by setting five bin width levels for CT values. Reproducible features were selected by the intraclass correlation coefficient (ICC). Radiomic features were compared between datasets NE and CE. Subgroup analyses were performed based on the CT acquisition period, exposure value, and patient characteristics.ResultsEighty features were considered reproducible (0.5 ≤ ICC). Twelve of the sixteen first-order features, independent of the bin width levels, were statistically different between datasets NE and CE (p < 0.05), and the p-values of two first-order features depending on the bin width levels were reduced with narrower bin widths. Sixteen out of sixty-two features showed a significant difference, regardless of the bin width (p < 0.05). There were significant differences between datasets NE and CE with older age, lighter body weight, better performance status, being a smoker, larger gross tumor volume, and tumor location at central region.ConclusionsContrast enhancement in the delayed phase of CT images for NSCLC patients affected some of the radiomic features and the variability of radiomic features due to contrast uptake may depend largely on the patient characteristics.     

Exploration of temporal stability and prognostic power of radiomic features based on electronic portal imaging device images

PurposeWe aimed to explore the temporal stability of radiomic features in the presence of tumor motion and the prognostic powers of temporally stable features.MethodsWe selected single fraction dynamic electronic portal imaging device (EPID) (n = 275 frames) and static digitally reconstructed radiographs (DRRs) of 11 lung cancer patients, who received stereotactic body radiation therapy (SBRT) under free breathing. Forty-seven statistical radiomic features, which consisted of 14 histogram-based features and 33 texture features derived from the graylevel co-occurrence and graylevel run-length matrices, were computed. The temporal stability was assessed by using a multiplication of the intra-class correlation coefficients (ICCs) between features derived from the EPID and DRR images at three quantization levels. The prognostic powers of the features were investigated using a different database of lung cancer patients (n = 221) based on a Kaplan-Meier survival analysis.ResultsFifteen radiomic features were found to be temporally stable for various quantization levels. Among these features, seven features have shown potentials for prognostic prediction in lung cancer patients.ConclusionsThis study suggests a novel approach to select temporally stable radiomic features, which could hold prognostic powers in lung cancer patients.     

Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer

PurposeRadiomic models have been demonstrated to have acceptable discrimination capability for detecting lymph node metastasis (LNM). We aimed to develop a computed tomography–based radiomic model and validate its usefulness in the prediction of normal-sized LNM at node level in cervical cancer.MethodsA total of 273 LNs of 219 patients from 10 centers were evaluated in this study. We randomly divided the LNs from the 2 centers with the largest number of LNs into the training and internal validation cohorts, and the rest as the external validation cohort. Radiomic features were extracted from the arterial and venous phase images. We trained an artificial neural network (ANN) to develop two single-phase models. A radiomic model reflecting the features of two-phase images was also built for directly predicting LNM in cervical cancer. Moreover, four state-of-the-art methods were used for comparison. The performance of all models was assessed using the area under the receiver operating characteristic curve (AUC).ResultsAmong the models we built, the models combining the features of two phases surpassed the single-phase models, and the models generated by ANN had better performance than the others. We found that the radiomic model achieved the highest AUCs of 0.912 and 0.859 in the training and internal validation cohorts, respectively. In the external validation cohort, the AUC of the radiomic model was 0.800.ConclusionWe constructed a radiomic model that exhibited great ability in the prediction of LNM. The application of the model could optimize clinical staging and decision-making.     

Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy

ObjectivesBreast cancers show different regression patterns after neoadjuvant chemotherapy. Certain regression patterns are associated with more reliable margins in breast-conserving surgery. Our study aims to establish a nomogram based on radiomic features and clinicopathological factors to predict regression patterns in breast cancer patients.MethodsWe retrospectively reviewed 144 breast cancer patients who received neoadjuvant chemotherapy and underwent definitive surgery in our center from January 2016 to December 2019. Tumor regression patterns were categorized as type 1 (concentric regression + pCR) and type 2 (multifocal residues + SD + PD) based on pathological results. We extracted 1158 multidimensional features from 2 sequences of MRI images. After feature selection, machine learning was applied to construct a radiomic signature. Clinical characteristics were selected by backward stepwise selection. The combined prediction model was built based on both the radiomic signature and clinical factors. The predictive performance of the combined prediction model was evaluated.ResultsTwo radiomic features were selected for constructing the radiomic signature. Combined with two significant clinical characteristics, the combined prediction model showed excellent prediction performance, with an area under the receiver operating characteristic curve of 0.902 (95% confidence interval 0.8343–0.9701) in the primary cohort and 0.826 (95% confidence interval 0.6774–0.9753) in the validation cohort.ConclusionsOur study established a unique model combining a radiomic signature and clinicopathological factors to predict tumor regression patterns prior to the initiation of NAC. The early prediction of type 2 regression offers the opportunity to modify preoperative treatments or aids in determining surgical options.     

The Potential Value of Preoperative MRI Texture and Shape Analysis in Grading Meningiomas: A Preliminary Investigation

OBJECT: Preoperative knowledge of meningioma grade is essential for planning treatment and surgery. The purpose of this study was to investigate the diagnostic value of MRI texture and shape analysis in grading meningiomas. METHODS: A surgical database was reviewed to identify meningioma patients who had undergone tumor resection between January 2015 and December 2016. Preoperative MR images were retrieved and analyzed. Texture and shape analysis was conducted to quantitatively evaluate tumor heterogeneity and morphology. Three machine learning classifiers were trained with these features to build classification models. The performance of the features and classification models was assessed. RESULTS: A total of 131 patients were included in this study: 21 with high-grade meningiomas and 110 with low-grade meningiomas. Three texture features were selected: Horzl_RLNonUni, S(2,2)SumOfSqs, and WavEnHL_s-3; three shape features were selected: GeoFv, GeoW4, and GeoW5b. The Mann–Whitney test indicated that all six features were significantly different between high-grade and low-grade meningiomas. AUC values were generally greater than 0.50 (range, 0.73 to 0.88). Sensitivities and specificities ranged from 47.62% to 90.48% and 69.09% to 96.36%, respectively. Among the nine classification models obtained, the one built by training the SVM classifier with all six features achieved the best performance, with a sensitivity, specificity, diagnostic accuracy, and AUC of 0.86, 0.87, 0.87, and 0.87, respectively. CONCLUSIONS: Texture and shape analysis, especially when combined with a SVM classifier, can provide satisfactory performance in the preoperative determination of meningioma grade and is thus potentially useful for clinical application.     

苏铁属植物叶表皮特征及其分类学和古生态学意义

苏铁属(Cycas)作为一类古老的裸子植物,经历了漫长的演化历史,因此,深入研究其形态特征与环境的相互关系,有望为古环境重建提供重要参考依据。本文分析西双版纳和深圳两地植物园栽培环境下27种苏铁属植物的叶表皮特征及气孔参数的差异,并进一步探讨气孔参数与系统发育的关系。结果表明:(1)苏铁属内叶表皮特征保守稳定,具有一定的分类学意义:依据表皮细胞及气孔器特征划分了四种叶表皮类型,可为苏铁现生植物或化石的鉴定提供参考。(2)四种叶表皮类型指示了不同的原生生境特征,对古环境具有一定的指示意义。(3)同一环境下,气孔参数在属内的种间差异显著,其次,气孔指数在属内变化与系统发育有关,除气孔指数具显著的系统发育信号外,其余气孔参数均无显著系统发育信号。本研究结果表明气孔参数法重建古大气CO2浓度时,需尽可能利用亲缘关系相近、叶表皮和生境皆相似的代理种,并明确气孔参数与大气CO2分压的相关关系在种间的异同,从而提高该方法的有效性。     

Application of radiography of computed tomography in non-small cell lung cancer using prognosis model

ObjectiveStudying the diagnostic value of CT imaging in non-small cell lung cancer (NSCLC), and establishing a prognosis model combined with clinical characteristics is the objective, so as to provide a reference for the survival prediction of NSCLC patients.MethodCT scan data of NSCLC 200 patients were taken as the research object. Through image segmentation, the radiology features of CT images were extracted. The reliability and performance of the prognosis model based on the optimal feature number of specific algorithm and the prognosis model based on the global optimal feature number were compared.Results30-RELF-NB (30 optimal features, RELF feature selection algorithm and NB classifier) has the highest accuracy and AUC (area under the subject characteristic curve) in the prognosis model based on the optimal features of specific algorithm. Among the prognosis models based on global optimal features, 25-NB (25 global optimal features, naive Bayes classification algorithm classifier) has the highest accuracy and AUC. Compared with the prediction model based on feature training of specific feature selection algorithm, the overall performance and stability of the prediction model based on global optimal feature are higher.ConclusionThe prognosis model based on the global optimal feature established in this paper has good reliability and performance, and can be applied to the CT radiology of NSCLC.     

The Chemokine Receptor CXCR4 and c-MET Cooperatively Promote Epithelial-Mesenchymal Transition in Gastric Cancer Cells

The C-X-C motif chemokine receptor 4 (CXCR4) pathway can promote tumor metastasis but is dependent on cross talk with other signaling pathways. The MET proto-oncogene (c-MET) participates in metastasis and is highly expressed in gastric cancer. However, the relationship between CXCR4 and c-MET signaling and their mechanisms of action in gastric cancer metastasis remain unclear. In this study, in vitro experiments demonstrated that C-X-C motif chemokine ligand 12 (CXCL12)/CXCR4 induces epithelial-mesenchymal transition (EMT) and promotes migration in gastric cancer cells, which is accompanied by c-MET activation. These phenomena were reversed by c-MET inhibition. Further investigation revealed that c-MET activation correlated with its interaction with caveolin 1 in lipid rafts, induced by CXCL12. In clinical samples, we observed a significant positive association between CXCR4 expression and c-MET phosphorylation (r = 0.259, P = .005). Moreover, samples expressing both receptors were found to indicate significantly poorer patient prognosis (P < .001). These results suggest that CXCL12 induces EMT at least partially through cross talk between CXCR4 and c-MET signaling. In addition, changes in these pathways could have clinical importance for the treatment of gastric cancer.     

Genetic and Clinical Characteristics of Phyllodes Tumors of the Breast

PURPOSE: Phyllodes tumors (PTs) of the breast are rare, accounting for less than 1% of all breast tumors. Among PTs, malignant PTs (MPTs) have malignant characteristics and distant metastases occur in about 20% to 30% of MPTs. However, there is no effective treatment for MPTs with distant metastasis, resulting in an abject prognosis. We performed targeted deep sequencing on PTs to identify the associations between genetic alterations and clinical prognosis. METHODS: We performed targeted deep sequencing to evaluate the genetic characteristics of PTs and analyzed the relationships between clinical and genetic characteristics. RESULTS: A total of 17 PTs were collected between 2001 and 2012. Histologic review was performed by pathologists. The samples included three benign PTs, one borderline PT, and 13 MPTs. The most frequently detected genetic alteration occurred in the TERT promoter region (70.6%), followed by MED12 (64.7%). EGFR amplification and TP53 alteration were detected in four MPTs without genetic alterations in MED12 and TERT promoter regions. Genetic alterations of RARA and ZNF703 were repeatedly found in PTs with local recurrence, and genetic alterations of SETD2, BRCA2, and TSC1 were detected in PTs with distant metastasis. Especially, MPT harboring PTEN and RB1 copy number deletion showed rapid disease progression. CONCLUSIONS: In this study, we provide genetic characterization and potential therapeutic target for this rare, potentially lethal disease. Further large-scale comprehensive genetic study and functional validation are warranted.     

Preoperative Monocyte-to-Lymphocyte Ratio in Peripheral Blood Predicts Stages,Metastasis, and Histological Grades in Patients with Ovarian Cancer

PURPOSE: The monocyte-to-lymphocyte ratio (MLR) has been shown to be associated with the prognosis of various solid tumors. This study sought to evaluate the important value of the MLR in ovarian cancer patients. METHODS: A total of 133 ovarian cancer patients and 43 normal controls were retrospectively reviewed. The patients'' demographics were analyzed along with clinical and pathologic data. The counts of peripheral neutrophils, lymphocytes, monocytes, and platelets were collected and used to calculate the MLR, neutrophil-to-lymphocyte ratio (NLR). and platelet-to-lymphocyte ratio (PLR). The optimal cutoff value of the MLR was determined by using receiver operating characteristic curve analysis. We compared the MLR, NLR, and PLR between ovarian cancer and normal control patients and among patients with different stages and different grades, as well as between patients with lymph node metastasis and non–lymph node metastasis. We then investigated the value of the MLR in predicting the stage, grade, and lymph node positivity by using logistic regression. The impact of the MLR on overall survival (OS) was calculated by Kaplan-Meier method and compared by log-rank test. RESULTS: Statistically significant differences in the MLR were observed between ovarian cancer patients and normal controls. However, no difference was found for the NLR and PLR. Highly significant differences in the MLR were found among patients with different stages (stage I-II and stage III-IV), grades (G1 and >G1), and lymph node metastasis status. The MLR was a significant and independent risk factor for lymph node metastasis, as determined by logistic regression. The optimal cutoff value of the MLR was 0.23. We also classified the data according to tumor markers (CA125, CA199, HE4, AFP, and CEA) and conventional coagulation parameters (International Normalized Ratio [INR] and fibrinogen). Highly significant differences in CA125, CA199, HE4, INR, fibrinogen levels, and lactate dehydrogenase were found between the low-MLR group (MLR ≤ 0.23) and the high-MLR group (MLR > 0.23). Correspondingly, dramatic differences were observed between the two groups in OS. CONCLUSION: Our results show that the peripheral blood MLR before surgery could be a significant predictor of advanced stages, advanced pathologic grades, and positive lymphatic metastasis in ovarian cancer patients.     

Clinical Features of Brain Metastases in Small Cell Lung Cancer: an Implication for Hippocampal Sparing Whole Brain Radiation Therapy

PURPOSE: To assess the clinical features and distribution of brain metastases (BMs) of small cell lung cancer (SCLC) in the hippocampal and perihippocampal region, with the purpose of exploring the viability of hippocampal-sparing whole-brain radiation therapy (HS-WBRT) on reducing neurocognitive deficits. METHODS: This was a retrospective analysis of the clinical characteristics and patterns of BMs in patients with SCLC. Associations between the clinical characteristics and hippocampal metastases (HMs)/perihippocampal metastases (PHMs) were evaluated in univariate and multivariate regression analyses. RESULTS: A total of 1594 brain metastatic lesions were identified in 180 patients. Thirty-two (17.8%) patients were diagnosed with BMs at the time of primary SCLC diagnosis. The median interval between diagnosis of primary SCLC and BMs was 9.3 months. There were 9 (5.0%) and 22 (12.2%) patients with HMs and PHMs (patients with BMs located in or within 5 mm around the hippocampus), respectively. In the univariate and multivariate analysis, the number of BMs was the risk factor for HMs and PHMs. Patients with BMs ≥ 5 had significantly higher risk of HMs (odds ratio [OR] 7.892, 95% confidence interval [CI] 1.469-42.404, P = .016), and patients with BMs ≥ 7 had significantly higher risk of PHMs (OR 5.162, 95% CI 2.017-13.213, P = .001). Patients with extracranial metastases are also associated with HMs. CONCLUSIONS: Our results indicate that patients with nonoligometastatic disease are significantly associated with HMs and PHMs. The incidence of PHMs may be acceptably low enough to perform HS-WBRT for SCLC. Our findings provide valuable clinical data to assess the benefit of HS-WBRT in SCLC patients with BMs.     

肺癌转移与中医脏象理论相关性分析

目的:分析肺癌转移与中医脏象理论的相关性。方法:回顾性选择我院收治的102例晚期肺癌转移患者为研究对象,统计患者的转移灶所属部位,并参照《中医内科学》对患者的中医临床证候进行判定。分别统计各证型患者肺癌转移的单器官及首发转移器官的分布、肺癌转移各单器官及首发转移器官的证型分布情况。结果:各证型患者肺癌转移的单器官及首发转移器官分布率比较,差异具有统计学意义(P0.05)。同时,肺癌转移各单器官及首发转移器官的证型分布率比较,差异具有统计学意义(P0.05)。结论:根据肺癌患者的中医临床证候可判断其病灶转移方向,同时,根据肺癌患者的转移灶所属部位可判断其中医临床证型。     

Molecular Imaging of Pancreatic Duct Adenocarcinoma Using a Type 2 Cannabinoid Receptor-Targeted Near-Infrared Fluorescent Probe

Imaging probes targeting type 2 cannabinoid receptor (CB₂R) overexpressed in pancreatic duct adenocarcinoma (PDAC) tissue have the potential to improve early detection and surgical outcome of PDAC. The aim of our study was to evaluate the molecular imaging potential of a CB₂R-targeted near-infrared (NIR) fluorescent probe (NIR760-XLP6) for PDAC. CB₂R overexpression was observed in both PDAC patient tissues and various pancreatic cancer cell lines. In vitro fluorescence imaging indicated specific binding of NIR760-XLP6 to CB₂R in human PDAC PANC-1 cells. In a xenograft mouse tumor model, NIR760-XLP6 showed remarkable 50- (ex vivo) and 3.2-fold (in vivo) tumor to normal contrast enhancement with minimal liver and kidney uptake. In a PDAC lymph node metastasis model, significant signal contrast was observed in bilateral axillary lymph nodes with PDAC metastasis after injection of the probe. In conclusion, NIR760-XLP6 exhibits promising characteristics for imaging PDAC, and CB₂R appears to be an attractive target for PDAC imaging.     

上皮性卵巢癌患者CT、MRI影像学特征及与血清标志物CEA、CA199、CA125水平的相关性研究

摘要 目的：探讨上皮性卵巢癌患者电子计算机断层扫描（CT）、磁共振成像（MRI）影像学特征及与血清标志物癌胚抗原（CEA）、糖类抗原199（CA199）、糖类抗原125（CA125）水平的相关性。方法：回顾性分析2014年4月-2020年2月于我院83例诊断为上皮性卵巢癌患者的CT、MRI影像学资料,以手术病理结果作为金标准。分析患者的CT、MRI影像学特征,检测患者血清CEA、CA199、CA125水平,评价患者CT、MRI影像学特征与血清CEA、CA199、CA125水平的相关性。结果：上皮性卵巢癌肿瘤横截面最大径为14.2mm-121.7mm,平均（18.6±4.3）mm,上皮性卵巢癌以混杂密度/信号为主,形态不规则,病灶多为囊实性,可见壁结节及分隔改变,增强后可见分隔或壁结节明显强化,可伴有腹水、腹膜转移、淋巴结转移。血清CEA、CA199、CA125水平分别为（66.35±7.52）ng/mL、（183.59±22.62）U/mL、（225.27±25.34）U/mL。上皮性卵巢癌边界清晰、不清晰的血清CA199、CA125水平组间差异有统计学意义（P＜0.05）;上皮性卵巢癌形态圆形/类圆形/椭圆形、分叶状、形态不规则的血清CA199、CA125水平组间差异有统计学意义（P＜0.05）;上皮性卵巢癌患者有壁结节、腹膜转移、淋巴结转移的血清CEA、CA199、CA125水平组间差异有统计学意义（P＜0.05）;其余CT、MRI影像学表现特征组间血清CEA、CA199、CA125水平差异无统计学意义（P＞0.05）。上皮性卵巢癌边界与血清CA125水平呈正相关（P＜0.05）,上皮性卵巢癌形态与血清CA199、CA125水平呈正相关（P＜0.05）,壁结节与血清CA125水平呈正相关（P＜0.05）,腹膜转移、淋巴结转移与血清CEA、CA199、CA125水平呈正相关（P＜0.05）,其余指标之间无明显相关性（P＞0.05）。结论：上皮性卵巢癌CT、MRI影像表现具有特征性,血清CEA、CA199、CA125水平的检测有助于对早期上皮性卵巢癌的诊断以及不同病理类型的判断,CT、MRI影像学特征与血清CEA、CA199、CA125水平具有相关性,可判断疾病的进展及患者预后情况,对指导临床综合治疗及评估患者预后可提供客观依据。     

Early risk-assessment of patients with nasopharyngeal carcinoma: the added prognostic value of MR-based radiomics

ObjectivesTo assess the additive prognostic value of MR-based radiomics in predicting progression-free survival (PFS) in patients with nasopharyngeal carcinoma (NPC)MethodsPatients newly diagnosed with non-metastatic NPC between June 2006 and October 2019 were retrospectively included and randomly grouped into training and test cohorts (7:3 ratio). Radiomic features (n=213) were extracted from T2-weighted and contrast-enhanced T1-weighted MRI. The patients were staged according to the 8^th edition of American Joint Committee on Cancer Staging Manual. The least absolute shrinkage and selection operator was used to select the relevant radiomic features. Univariate and multivariate Cox proportional hazards analyses were conducted for PFS, yielding three different survival models (clinical, stage, and radiomic). The integrated time-dependent area under the curve (iAUC) for PFS was calculated and compared among different combinations of survival models, and the analysis of variance was used to compare the survival models. The prognostic performance of all models was validated using a test set with integrated Brier scores.ResultsThis study included 81 patients (training cohort=57; test cohort=24), and the mean PFS was 57.5 ± 43.6 months. In the training cohort, the prognostic performances of survival models improved significantly with the addition of radiomics to the clinical (iAUC, 0.72–0.80; p=0.04), stage (iAUC, 0.70–0.79; p=0.001), and combined models (iAUC, 0.76–0.81; p<0.001). In the test cohort, the radiomics and combined survival models were robustly validated for their ability to predict PFS.ConclusionIntegration of MR-based radiomic features with clinical and stage variables improved the prediction PFS in patients diagnosed with NPC.